Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

• Published in: PloS one Vol. 9; no. 3; p. e91441
• Main Authors: Muthuraman, Muthuraman, Hellriegel, Helge, Hoogenboom, Nienke, Anwar, Abdul Rauf, Mideksa, Kidist Gebremariam, Krause, Holger, Schnitzler, Alfons, Deuschl, Günther, Raethjen, Jan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.03.2014; Public Library of Science (PLoS)
• Subjects: Adult; Beamforming; Biology; Brain; Brain - physiology; Brain Mapping; Coherence; Coherence analysis; Conduction; Connectivity analysis; Cortex; Depth indicators; Dipoles; EEG; Electroencephalography; Electromyography; Engineering; Female; Finger; Head Movements; Healthy Volunteers; Humans; Magnetoencephalography; Male; Medicine; Models, Neurological; Motor task performance; Movement - physiology; Neural networks; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; Physics; Physiology; Rhythms; Sensors; Signal reconstruction; Signal-To-Noise Ratio; Studies; System theory; Temporal resolution; Topography; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0091441

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.